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http://hdl.handle.net/2080/544
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DC Field | Value | Language |
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dc.contributor.author | Singh, A K | - |
dc.contributor.author | Panda, S S | - |
dc.contributor.author | Chakraborty, D | - |
dc.contributor.author | Pal, S K | - |
dc.date.accessioned | 2007-11-07T05:58:47Z | - |
dc.date.available | 2007-11-07T05:58:47Z | - |
dc.date.issued | 2006 | - |
dc.identifier.citation | The International Journal of Advanced Manufacturing Technology, Volume 28, Iss 5-6, P 456-462 | en |
dc.identifier.uri | http://dx.doi.org/10.1007/s00170-004-2376-0 | - |
dc.identifier.uri | http://hdl.handle.net/2080/544 | - |
dc.description | Copyright for the published version belongs to Springer | en |
dc.description.abstract | The present work deals with drill wear monitoring using artificial neural network. A back propagation neural network (BPNN) has been used to predict the flank wear of high speed steel (HSS) drill bit for drilling holes on copper work-piece. Experiments have been carried out over a wide range of cutting conditions and the effect of various process parameter like feeed-rate, spindle speed, drill diameter on thrust force and torque has been studied. The data thus obtained from the experiments have been used to train a BPNN for wear prediction. The performance of the trained neural network has been tested with the experimental data and found to be satisfactory. | en |
dc.format.extent | 1245184 bytes | - |
dc.format.mimetype | application/msword | - |
dc.language.iso | en | - |
dc.publisher | Springer | en |
dc.subject | Flant Wear | en |
dc.subject | Artificial Neural Network | en |
dc.subject | Drilling | en |
dc.title | Drill wear prediction using artificial neural network | en |
dc.type | Article | en |
Appears in Collections: | Journal Articles |
Files in This Item:
File | Description | Size | Format | |
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sspanda-IJAMT-1.doc | 1.22 MB | Microsoft Word | View/Open |
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